The Impact of Wind Speed on Electricity Prices in the Polish Day-Ahead Market Since 2016, and Its Applicability to Machine-Learning-Powered Price Prediction

Rafał Sowiński () and Aleksandra Komorowska
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Rafał Sowiński: Mineral and Energy Economy Research Institute of the Polish Academy of Sciences, ul. Wybickiego 7A, 31-261 Kraków, Poland
Aleksandra Komorowska: Mineral and Energy Economy Research Institute of the Polish Academy of Sciences, ul. Wybickiego 7A, 31-261 Kraków, Poland

Energies, 2025, vol. 18, issue 14, 1-27

Abstract: The rising share of wind generation in power systems, driven by the need to decarbonise the energy sector, is changing the relationship between wind speed and electricity prices. In the case of Poland, this relationship has not been thoroughly investigated, particularly in the aftermath of the restrictive legal changes introduced in 2016, which halted numerous onshore wind investments. Studying this relationship remains necessary to understand the broader market effects of wind speed on electricity prices, especially considering evolving policies and growing interest in renewable energy integration. In this context, this paper analyses wind speed, wind generation, and other relevant datasets in relation to electricity prices using multiple statistical methods, including correlation analysis, regression modelling, and artificial neural networks. The results show that wind speed is a significant factor in setting electricity prices (with a correlation coefficient reaching up to −0.7). The findings indicate that not only is it important to include wind speed as an electricity price indicator, but it is also worth investing in wind generation, since higher wind output can be translated into lower electricity prices. This study contributes to a better understanding of how natural variability in renewable resources translates into electricity market outcomes under policy-constrained conditions. Its innovative aspect lies in combining statistical and machine learning techniques to quantify the influence of wind speed on electricity prices, using updated data from a period of regulatory stagnation.

Keywords: wind speed; electricity prices; day-ahead market; wind generation; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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